miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are ~22 nt long integral elements responsible for post-transcriptional control of gene expressions. After the identification of thousands of miRNAs, the challenge is now to explore their specific biological functions. To this end, it will be greatly helpful to construct a reasonable organization of these miRNAs according to their homologous relationships. Given an established miRNA family system (e.g. the miRBase family organization), this paper addresses the problem of automatically and accurately classifying newly found miRNAs to their corresponding families by supervised learning techniques. Concretely, we propose an effective method, <it>miRFam</it>, which uses only primary information of pre-miRNAs or mature miRNAs and a multiclass SVM, to automatically classify miRNA genes.</p> <p>Results</p> <p>An existing miRNA family system prepared by miRBase was downloaded online. We first employed <it>n</it>-grams to extract features from known precursor sequences, and then trained a multiclass SVM classifier to classify new miRNAs (i.e. their families are unknown). Comparing with miRBase's sequence alignment and manual modification, our study shows that the application of machine learning techniques to miRNA family classification is a general and more effective approach. When the testing dataset contains more than 300 families (each of which holds no less than 5 members), the classification accuracy is around 98%. Even with the entire miRBase15 (1056 families and more than 650 of them hold less than 5 samples), the accuracy surprisingly reaches 90%.</p> <p>Conclusions</p> <p>Based on experimental results, we argue that <it>miRFam </it>is suitable for application as an automated method of family classification, and it is an important supplementary tool to the existing alignment-based small non-coding RNA (sncRNA) classification methods, since it only requires primary sequence information.</p> <p>Availability</p> <p>The source code of <it>miRFam</it>, written in C++, is freely and publicly available at: <url>http://admis.fudan.edu.cn/projects/miRFam.htm</url>.</p

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

An Automated Method to Enrich and Expand Consumer Health Vocabularies Using GloVe Word Embeddings

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this dissertation, we present an automatic method to enrich existing concepts in a medical ontology with additional laymen terms and also to expand the number of concepts in the ontology that do not have associated laymen terms. Our work has the benefit of being applicable to vocabularies in any domain. Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. We improve these vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. By performing iterative feedback using GloVe’s candidate terms, we can boost the number of word occurrences in the co-occurrence matrix allowing our approach to work with a smaller training corpus. Our novel algorithms and GloVe were evaluated using two laymen datasets from the National Library of Medicine (NLM), the Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV) and the MedlinePlus Healthcare Vocabulary. For our first goal, enriching concepts, the results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Our best algorithm enhanced the corpus with synonyms from WordNet, outperformed GloVe with an F-score relative improvement of 25%. For our second goal, expanding the number of concepts with related laymen’s terms, our synonym-enhanced GloVe outperformed GloVe with a relative F-score relative improvement of 63%. The results of the system were in general promising and can be applied not only to enrich and expand laymen vocabularies for medicine but any ontology for a domain, given an appropriate corpus for the domain. Our approach is applicable to narrow domains that may not have the huge training corpora typically used with word embedding approaches. In essence, by incorporating an external source of linguistic information, WordNet, and expanding the training corpus, we are getting more out of our training corpus. Our system can help building an application for patients where they can read their physician\u27s letters more understandably and clearly. Moreover, the output of this system can be used to improve the results of healthcare search engines, entity recognition systems, and many others

Modern considerations for the use of naive Bayes in the supervised classification of genetic sequence data

2021 Spring.Includes bibliographical references.Genetic sequence classification is the task of assigning a known genetic label to an unknown genetic sequence. Often, this is the first step in genetic sequence analysis and is critical to understanding data produced by molecular techniques like high throughput sequencing. Here, we explore an algorithm called naive Bayes that was historically successful in classifying 16S ribosomal gene sequences for microbiome analysis. We extend the naive Bayes classifier to perform the task of general sequence classification by leveraging advancements in computational parallelism and the statistical distributions that underlie naive Bayes. In Chapter 2, we show that our implementation of naive Bayes, called WarpNL, performs within a margin of error of modern classifiers like Kraken2 and local alignment. We discuss five crucial aspects of genetic sequence classification and show how these areas affect classifier performance: the query data, the reference sequence database, the feature encoding method, the classification algorithm, and access to computational resources. In Chapter 3, we cover the critical computational advancements introduced in WarpNL that make it efficient in a modern computing framework. This includes efficient feature encoding, introduction of a log-odds ratio for comparison of naive Bayes posterior estimates, description of schema for parallel and distributed naive Bayes architectures, and use of machine learning classifiers to perform outgroup sequence classification. Finally in Chapter 4, we explore a variant of the Dirichlet multinomial distribution that underlies the naive Bayes likelihood, called the beta-Liouville multinomial. We show that the beta-Liouville multinomial can be used to enhance classifier performance, and we provide mathematical proofs regarding its convergence during maximum likelihood estimation. Overall, this work explores the naive Bayes algorithm in a modern context and shows that it is competitive for genetic sequence classification